Unit testing Spark transformations in Apache Spark - Mini Project: Build & Apply

Unit Testing Spark Transformations

📖 Scenario: You work as a data engineer. You write Spark code to transform data. Your manager asks you to write tests to check your transformations work correctly.Testing helps catch mistakes early and keeps data reliable.

🎯 Goal: You will create a small Spark DataFrame, write a transformation function, and then write a unit test to check the transformation output.

📋 What You'll Learn

Create a Spark DataFrame with exact data

Write a configuration variable for filtering

Write a transformation function using Spark DataFrame API

Write a unit test that checks the transformation output

Print the test result as True or False

💡 Why This Matters

🌍 Real World

Data engineers and data scientists often write Spark code to process big data. Unit testing ensures their code works correctly before running expensive jobs.

💼 Career

Knowing how to write and test Spark transformations is a key skill for data engineers and data scientists working with big data platforms.

1

Create initial Spark DataFrame

Create a Spark DataFrame called df with these exact rows: (1, 'Alice', 29), (2, 'Bob', 31), (3, 'Charlie', 25). Use columns named 'id', 'name', and 'age'. Use SparkSession spark already available.

Apache Spark

from pyspark.sql import SparkSession
spark = SparkSession.builder.master('local').appName('test').getOrCreate()
# Create the DataFrame df with the exact rows and columns
# Your code here

Hint

Use spark.createDataFrame(data, columns) to create the DataFrame.

2

Add filter age threshold variable

Create a variable called age_threshold and set it to 30. This will be used to filter people older than this age.

Apache Spark

from pyspark.sql import SparkSession
spark = SparkSession.builder.master('local').appName('test').getOrCreate()
data = [(1, 'Alice', 29), (2, 'Bob', 31), (3, 'Charlie', 25)]
columns = ['id', 'name', 'age']
df = spark.createDataFrame(data, columns)
# Create age_threshold variable and set to 30
# Your code here

Hint

Just create a variable age_threshold and assign 30.

3

Write transformation function to filter DataFrame

Write a function called filter_by_age that takes a DataFrame df and an integer threshold. It returns a new DataFrame filtered to rows where age is greater than threshold. Use DataFrame API filter method.

Apache Spark

from pyspark.sql import SparkSession
spark = SparkSession.builder.master('local').appName('test').getOrCreate()
data = [(1, 'Alice', 29), (2, 'Bob', 31), (3, 'Charlie', 25)]
columns = ['id', 'name', 'age']
df = spark.createDataFrame(data, columns)
age_threshold = 30
# Write function filter_by_age(df, threshold) that returns filtered DataFrame
# Your code here

Hint

Use df.filter(df.age > threshold) inside the function.

4

Write unit test and print result

Write code to test filter_by_age with df and age_threshold. Collect the result to a list of rows. Create a list expected with one tuple (2, 'Bob', 31). Print True if the result matches expected, else False.

Apache Spark

from pyspark.sql import SparkSession
spark = SparkSession.builder.master('local').appName('test').getOrCreate()
data = [(1, 'Alice', 29), (2, 'Bob', 31), (3, 'Charlie', 25)]
columns = ['id', 'name', 'age']
df = spark.createDataFrame(data, columns)
age_threshold = 30
def filter_by_age(df, threshold):
    return df.filter(df.age > threshold)
# Test filter_by_age and print True if output matches expected, else False
# Your code here

Hint

Use collect() to get rows as a list. Compare with expected list and print the boolean result.

Practice

1. What is the main purpose of unit testing Spark transformations?

easy

A. To check small parts of Spark code for errors early

B. To improve the speed of Spark jobs

C. To deploy Spark applications automatically

D. To visualize Spark data outputs

Solution

1. Step 1: Understand unit testing purpose

   Unit testing focuses on checking small parts of code to find errors early.

2. Step 2: Relate to Spark transformations

   Testing Spark transformations means verifying each step works correctly before full runs.

3. Final Answer:

   To check small parts of Spark code for errors early -> Option A

4. Quick Check:

   Unit testing = Early error detection [OK]

Hint: Unit tests catch errors early in small code parts [OK]

Common Mistakes:

• Confusing unit testing with performance tuning
• Thinking unit tests deploy code
• Assuming unit tests create visualizations

2. Which of the following is the correct way to compare actual and expected DataFrames in a Spark unit test?

easy

A. print(actual_df) == print(expected_df)

B. assert actual_df.collect() == expected_df.collect()

C. actual_df.show() == expected_df.show()

D. actual_df.count() != expected_df.count()

Solution

1. Step 1: Understand DataFrame comparison

   Comparing DataFrames requires checking their collected data, not just printing or counts.

2. Step 2: Identify correct assertion

   Using assert with collected rows compares actual data content correctly.

3. Final Answer:

   assert actual_df.collect() == expected_df.collect() -> Option B

4. Quick Check:

   Compare collected data for equality [OK]

Hint: Use collect() to compare DataFrame contents in tests [OK]

Common Mistakes:

• Comparing printed output instead of data
• Using show() which returns None
• Comparing only row counts without data

3. Given the following Spark code in a unit test:

input_df = spark.createDataFrame([(1, 'a'), (2, 'b')], ['id', 'value'])
transformed_df = input_df.filter("id > 1")
result = transformed_df.collect()

What is the value of result?

medium

A. [Row(id=1, value='a')]

B. []

C. [Row(id=1, value='a'), Row(id=2, value='b')]

D. [Row(id=2, value='b')]

Solution

1. Step 1: Understand the filter condition

   The filter "id > 1" keeps rows where id is greater than 1, so only id=2 remains.

2. Step 2: Collect the filtered rows

   Collect returns a list of Row objects matching the filter, here only one row with id=2 and value='b'.

3. Final Answer:

   [Row(id=2, value='b')] -> Option D

4. Quick Check:

   Filter id > 1 keeps only id=2 [OK]

Hint: Filter keeps rows matching condition before collect [OK]

Common Mistakes:

• Including rows with id=1 mistakenly
• Confusing collect output format
• Assuming filter excludes id=2

4. You wrote this unit test code:

expected_df = spark.createDataFrame([(1, 'x')], ['id', 'value'])
actual_df = spark.createDataFrame([(1, 'x')], ['id', 'value'])
assert expected_df == actual_df

Why does this test fail?

medium

A. DataFrames cannot be compared directly with '=='

B. The schemas of DataFrames are different

C. The data in DataFrames is different

D. Spark session is not initialized

Solution

1. Step 1: Understand DataFrame equality

   Spark DataFrames do not support direct '==' comparison; it checks object identity, not content.

2. Step 2: Correct way to compare DataFrames

   Use methods like collect() or exceptAll() to compare data content, not '=='.

3. Final Answer:

   DataFrames cannot be compared directly with '==' -> Option A

4. Quick Check:

   Use collect() for DataFrame equality, not '==' [OK]

Hint: Never use '==' to compare DataFrames directly [OK]

Common Mistakes:

• Assuming '==' compares data content
• Ignoring schema differences
• Forgetting to initialize Spark session

5. You want to unit test a Spark transformation that removes duplicate rows based on the 'id' column, keeping the first occurrence. Which approach correctly tests this transformation?

hard

A. Create input DataFrame without duplicates, apply transformation, assert output equals input

B. Create input DataFrame with duplicates, apply transformation, assert output count equals input count

C. Create input DataFrame with duplicates, apply transformation, assert output equals expected DataFrame without duplicates

D. Create input DataFrame with duplicates, apply transformation, assert output is empty

Solution

1. Step 1: Prepare input with duplicates

   To test duplicate removal, input must contain duplicate rows on 'id'.

2. Step 2: Apply transformation and compare output

   After transformation, output should have duplicates removed; compare with expected DataFrame without duplicates.

3. Final Answer:

   Create input DataFrame with duplicates, apply transformation, assert output equals expected DataFrame without duplicates -> Option C

4. Quick Check:

   Test removes duplicates by comparing expected output [OK]

Hint: Test duplicate removal by comparing to expected no-duplicate DataFrame [OK]

Common Mistakes:

• Testing with no duplicates in input
• Comparing only row counts, not content
• Expecting empty output after removal